Identification of the coding region for a second poly(A) polymerase in Escherichia coli.

We had earlier identified the pcnB locus as the gene for the major Escherichia coli poly(A) polymerase (PAP I). In this report, we describe the disruption and identification of a candidate gene for a second poly(A) polymerase (PAP II) by an experimental strategy which was based on the assumption that the viability of E. coli depends on the presence of either PAP I or PAP II. The coding region thus identified is the open reading frame f310, located at about 87 min on the E. coli chromosome. The following lines of evidence support f310 as the gene for PAP II: (i) the deduced peptide encoded by f310 has a molecular weight of 36,300, similar to the molecular weight of 35,000 estimated by gel filtration of PAP II; (ii) the deduced f310 product is a relatively hydrophobic polypeptide with a pI of 9.4, consistent with the properties of partially purified PAP II; (iii) overexpression of f310 leads to the formation of inclusion bodies whose solubilization and renaturation yields poly(A) polymerase activity that corresponds to a 35-kDa protein as shown by enzyme blotting; and (iv) expression of a f310 fusion construct with hexahistidine at the N-terminus of the coding region allowed purification of a poly(A) polymerase fraction whose major component is a 36-kDa protein. E. coli PAP II has no significant sequence homology either to PAP I or to the viral and eukaryotic poly(A) polymerases, suggesting that the bacterial poly(A) polymerases have evolved independently. An interesting feature of the PAP II sequence is the presence of sets of two paired cysteine and histidine residues that resemble the RNA binding motifs seen in some other proteins.

Characterization of a second secreted IgE isoform and identification of an asymmetric pathway of IgE assembly.

A number of alternatively spliced epsilon transcripts have been detected in IgE-producing B cells, in addition to the mRNAs encoding the classical membrane and secreted IgE heavy (H) chains. In a recent study, we examined the protein products of three of these alternatively spliced isoforms and found that they are intracellularly retained and degraded because of their inability to assemble into complete IgE molecules. We have now similarly examined a more recently described epsilon mRNA species that is generated by splicing between a donor splice site immediately upstream of the stop codon in the H-chain constant region exon 4 (CH4) and an acceptor site located in the 3' part of the second membrane exon. We show that this isoform is efficiently secreted by both plasma cells and B lymphocytes and therefore represents a second secreted IgE isoform (epsilon S2). The epsilon S2 H chain is only six amino acids longer than the classical secreted Ig H chain (epsilon S1) and contains a C-terminal cysteine, which is a characteristic sequence feature of mu and alpha H chains. However, unlike IgM and IgA, the epsilon S2 C-terminal cysteine (Cys-554) does not induce polymerization of H2L2 molecules (where L is light chain), but rather creates a disulfide bond between the two H chains that increases the rate of association into covalently bound H2L2 monomers. This C-terminal cysteine also does not function as an intracellular retention element because the epsilon S2 isoform was secreted in amounts equal to that of the epsilon S1, both in B lymphocytes and in plasma cells. The epsilon S2 H chains secreted by B lymphocytes differed from the epsilon S1 H chains in the extent of glycosylation. Interestingly, a difference in glycosylation between B-lymphocytes and plasma cells was also noted for both isoforms. The presence of the Cys-554 also allowed the identification of a distinctive asymmetric pathway of IgE assembly, common to both types of epsilon H chains.

Identification of a plant serine-arginine-rich protein similar to the mammalian splicing factor SF2/ASF.

We show that the higher plant Arabidopsis thaliana has a serine-arginine-rich (SR) protein family whose members contain a phosphoepitope shared by the animal SR family of splicing factors. In addition, we report the cloning and characterization of a cDNA encoding a higher-plant SR protein from Arabidopsis, SR1, which has striking sequence and structural homology to the human splicing factor SF2/ASF. Similar to SF2/ASF, the plant SR1 protein promotes splice site switching in mammalian nuclear extracts. A novel feature of the Arabidopsis SR protein is a C-terminal domain containing a high concentration of proline, serine, and lysine residues (PSK domain), a composition reminiscent of histones. This domain includes a putative phosphorylation site for the mitotic kinase cyclin/p34cdc2.

Infrared identification of 2XMM J191043.4+091629.4

Context. We report the infrared identification of the X-ray source 2XMM J191043.4+091629.4, which was detected by XMM-Newton/EPIC in the vicinity of the Galactic supernova remnant W49B. Aims. The aim of this work is to establish the nature of the X-ray source 2XMM J191043.4+091629.4 studying both the infrared photometry and spectroscopy of the companion. Methods. We analysed UKIDSS images around the best position of the X-ray source and obtained spectra of the best candidate using NICS in the Telescopio Nazionale Galileo (TNG) 3.5-m telescope. We present photometric and spectroscopic TNG analyses of the infrared counterpart of the X-ray source, identifying emission lines in the K-band. The H-band spectra does not present any significant feature. Results. We have shown that the Brackett γ H i at 2.165 μm, and He i at 2.184 μm and at 2.058 μm are significantly present in the infrared spectrum. The CO bands are also absent from our spectrum. Based on these results and the X-ray characteristics of the source, we conclude that the infrared counterpart is an early B-type supergiant star with an E(B − V) = 7.6 ± 0.3 at a distance of 16.0 ± 0.5 kpc. This would be, therefore, the first high-mass X-ray binary in the Outer Arm at galactic longitudes of between 30° and 60°.

Discussing the physical meaning of the absorption feature at 2.1 keV in 4U 1538–52

High resolution X-ray spectroscopy is a powerful tool for studying the nature of the matter surrounding the neutron star in X-ray binaries and its interaction between the stellar wind and the compact object. In particular, absorption features in their spectra could reveal the presence of atmospheres of the neutron star or their magnetic field strength. Here we present an investigation of the absorption feature at 2.1 keV in the X-ray spectrum of the high mass X-ray binary 4U 1538–52 based on our previous analysis of the XMM-Newton data. We study various possible origins and discuss the different physical scenarios in order to explain this feature. A likely interpretation is that the feature is associated with atomic transitions in an O/Ne neutron star atmosphere or of hydrogen and helium like Fe or Si ions formed in the stellar wind of the donor.

A new method for identification of protein (sub)families in a set of proteins based on hydropathy distribution in proteins

Structural similarity among proteins is reflected in the distribution of hydropathicity along the amino acids in the protein sequence. Similarities in the hydropathy distributions are obvious for homologous proteins within a protein family. They also were observed for proteins with related structures, even when sequence similarities were undetectable. Here we present a novel method that employs the hydropathy distribution in proteins for identification of (sub)families in a set of (homologous) proteins. We represent proteins as points in a generalized hydropathy space, represented by vectors of specifically defined features. The features are derived from hydropathy of the individual amino acids. Projection of this space onto principal axes reveals groups of proteins with related hydropathy distributions. The groups identified correspond well to families of structurally and functionally related proteins. We found that this method accurately identifies protein families in a set of proteins, or subfamilies in a set of homologous proteins. Our results show that protein families can be identified by the analysis of hydropathy distribution, without the need for sequence alignment. (C) 2005 Wiley-Liss, Inc.

Identification and analysis of novel genes expressed in the mouse embryonic facial primordia

Craniofacial anomalies are a common feature of human congenital dysmorphology syndromes, suggesting that genes expressed in the developing face are likely to play a wider role in embryonic development. To facilitate the identification of genes involved in embryogenesis, we previously constructed an enriched cDNA library by subtracting adult mouse liver cDNA from that of embryonic day (E)10.5 mouse pharyngeal arch cDNA. From this library, 273 unique clones were sequenced and known proteins binned into functional categories in order to assess enrichment of the library (1). We have now selected 31 novel and poorly characterised genes from this library and present bioinformatic analysis to predict proteins encoded by these genes, and to detect evolutionary conservation. Of these genes 61% (19/31) showed restricted expression in the developing embryo, and a subset of these was chosen for further in silico characterisation as well as experimental determination of subcellular localisation based on transient transfection of predicted full-length coding sequences into mammalian cell lines. Where a human orthologue of these genes was detected, chromosomal localisation was determined relative to known loci for human congenital disease.

Customer information system data pre-processing with feature selection techniques for non-technical losses prediction in an electricity market

Non-technical losses (NTL) identification and prediction are important tasks for many utilities. Data from customer information system (CIS) can be used for NTL analysis. However, in order to accurately and efficiently perform NTL analysis, the original data from CIS need to be pre-processed before any detailed NTL analysis can be carried out. In this paper, we propose a feature selection based method for CIS data pre-processing in order to extract the most relevant information for further analysis such as clustering and classifications. By removing irrelevant and redundant features, feature selection is an essential step in data mining process in finding optimal subset of features to improve the quality of result by giving faster time processing, higher accuracy and simpler results with fewer features. Detailed feature selection analysis is presented in the paper. Both time-domain and load shape data are compared based on the accuracy, consistency and statistical dependencies between features.

Event trigger identification for biomedical events extraction using domain knowledge

Motivation: In molecular biology, molecular events describe observable alterations of biomolecules, such as binding of proteins or RNA production. These events might be responsible for drug reactions or development of certain diseases. As such, biomedical event extraction, the process of automatically detecting description of molecular interactions in research articles, attracted substantial research interest recently. Event trigger identification, detecting the words describing the event types, is a crucial and prerequisite step in the pipeline process of biomedical event extraction. Taking the event types as classes, event trigger identification can be viewed as a classification task. For each word in a sentence, a trained classifier predicts whether the word corresponds to an event type and which event type based on the context features. Therefore, a well-designed feature set with a good level of discrimination and generalization is crucial for the performance of event trigger identification. Results: In this article, we propose a novel framework for event trigger identification. In particular, we learn biomedical domain knowledge from a large text corpus built from Medline and embed it into word features using neural language modeling. The embedded features are then combined with the syntactic and semantic context features using the multiple kernel learning method. The combined feature set is used for training the event trigger classifier. Experimental results on the golden standard corpus show that >2.5% improvement on F-score is achieved by the proposed framework when compared with the state-of-the-art approach, demonstrating the effectiveness of the proposed framework. © 2014 The Author 2014. The source code for the proposed framework is freely available and can be downloaded at http://cse.seu.edu.cn/people/zhoudeyu/ETI_Sourcecode.zip.

PDNAsite:identification of DNA-binding site from protein sequence by incorporating spatial and sequence context

Protein-DNA interactions are involved in many fundamental biological processes essential for cellular function. Most of the existing computational approaches employed only the sequence context of the target residue for its prediction. In the present study, for each target residue, we applied both the spatial context and the sequence context to construct the feature space. Subsequently, Latent Semantic Analysis (LSA) was applied to remove the redundancies in the feature space. Finally, a predictor (PDNAsite) was developed through the integration of the support vector machines (SVM) classifier and ensemble learning. Results on the PDNA-62 and the PDNA-224 datasets demonstrate that features extracted from spatial context provide more information than those from sequence context and the combination of them gives more performance gain. An analysis of the number of binding sites in the spatial context of the target site indicates that the interactions between binding sites next to each other are important for protein-DNA recognition and their binding ability. The comparison between our proposed PDNAsite method and the existing methods indicate that PDNAsite outperforms most of the existing methods and is a useful tool for DNA-binding site identification. A web-server of our predictor (http://hlt.hitsz.edu.cn:8080/PDNAsite/) is made available for free public accessible to the biological research community.

Automatic Identification of Large Fragments in a Pile of Broken Rock Using a Time-of-Flight Camera

This paper presents a solution to part of the problem of making robotic or semi-robotic digging equipment less dependant on human supervision. A method is described for identifying rocks of a certain size that may affect digging efficiency or require special handling. The process involves three main steps. First, by using range and intensity data from a time-of-flight (TOF) camera, a feature descriptor is used to rank points and separate regions surrounding high scoring points. This allows a wide range of rocks to be recognized because features can represent a whole or just part of a rock. Second, these points are filtered to extract only points thought to belong to the large object. Finally, a check is carried out to verify that the resultant point cloud actually represents a rock. Results are presented from field testing on piles of fragmented rock. Note to Practitioners—This paper presents an algorithm to identify large boulders in a pile of broken rock as a step towards an autonomous mining dig planner. In mining, piles of broken rock can contain large fragments that may need to be specially handled. To assess rock piles for excavation, we make use of a TOF camera that does not rely on external lighting to generate a point cloud of the rock pile. We then segment large boulders from its surface by using a novel feature descriptor and distinguish between real and false boulder candidates. Preliminary field experiments show promising results with the algorithm performing nearly as well as human test subjects.

Feature Study on a Programmable Network Traffic Classifier

Monitoring and tracking of IP traffic flows are essential for network services (i.e. packet forwarding). Packet header lookup is the main part of flow identification by determining the predefined matching action for each incoming flow. In this paper, an improved header lookup and flow rule update solution is investigated. A detailed study of several well-known lookup algorithms reveals that searching individual packet header field and combining the results achieve high lookup speed and flexibility. The proposed hybrid lookup architecture is comprised of various lookup algorithms, which are selected based on the user applications and system requirements.

Person Re-Identification using Deep Convnets with Multi-task Learning

Person re-identification involves recognizing a person across non-overlapping camera views, with different pose, illumination, and camera characteristics. We propose to tackle this problem by training a deep convolutional network to represent a person’s appearance as a low-dimensional feature vector that is invariant to common appearance variations encountered in the re-identification problem. Specifically, a Siamese-network architecture is used to train a feature extraction network using pairs of similar and dissimilar images. We show that use of a novel multi-task learning objective is crucial for regularizing the network parameters in order to prevent over-fitting due to the small size the training dataset. We complement the verification task, which is at the heart of re-identification, by training the network to jointly perform verification, identification, and to recognise attributes related to the clothing and pose of the person in each image. Additionally, we show that our proposed approach performs well even in the challenging cross-dataset scenario, which may better reflect real-world expected performance. 

Identification and characterization of parasitism genes from the pinewood nematode Bursaphelenchus xylophilus reveals a multilayered detoxification strategy.

The migratory endoparasitic nematode Bursaphelenchus xylophilus, which is the causal agent of pine wilt disease, has phytophagous and mycetophagous phases during its life cycle. This highly unusual feature distinguishes it from other plantparasitic nematodes and requires profound changes in biology between modes. During the phytophagous stage, the nematode migrates within pine trees, feeding on the contents of parenchymal cells. Like other plant pathogens, B. xylophilus secretes effectors from pharyngeal gland cells into the host during infection.We provide the first description of changes in the morphology of these gland cells between juvenile and adult life stages. Using a comparative transcriptomics approach and an effector identification pipeline, we identify numerous novel parasitism genes which may be important for the mediation of interactions of B. xylophilus with its host. In-depth characterization of all parasitism genes using in situ hybridization reveals two major categories of detoxification proteins, those specifically expressed in either the pharyngeal gland cells or the digestive system. These data suggest that B. xylophilus incorporates effectors in a multilayer detoxification strategy in order to protect itself from host defence responses during phytophagy.

Analysis of Pancreas Histological Images for Glucose Intolerance Identification using Wavelet Decomposition

Subtle structural differencescan be observed in the islets of Langer-hans region of microscopic image of pancreas cell of the rats having normal glucose tolerance and the rats having pre-diabetic(glucose intolerant)situa-tions. This paper proposes a way to automatically segment the islets of Langer-hans region fromthe histological image of rat's pancreas cell and on the basis of some morphological feature extracted from the segmented region the images are classified as normal and pre-diabetic.The experiment is done on a set of 134 images of which 56 are of normal type and the rests 78 are of pre-diabetictype. The work has two stages: primarily,segmentationof theregion of interest (roi)i.e. islets of Langerhansfrom the pancreatic cell and secondly, the extrac-tion of the morphological featuresfrom the region of interest for classification. Wavelet analysis and connected component analysis method have been used for automatic segmentationof the images. A few classifiers like OneRule, Naïve Bayes, MLP, J48 Tree, SVM etc.are used for evaluation among which MLP performed the best.